About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Taught by: Christopher Brooks
| Basic Info | Course 1 of 5 in the Applied Data Science with Python Specialization |
| Level | Intermediate |
| Language | English, Subtitles: Vietnamese |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Introduction to Specialization
- Reading: Syllabus
- Reading: Help us learn more about you!
- Video: Data Science
- Reading: 50 years of Data Science, David Donoho (optional)
- Video: The Coursera Jupyter Notebook System
- Notebook: Week 1 Lectures Jupyter Notebook
- Video: Python Functions
- Video: Python Types and Sequences
- Video: Python More on Strings
- Video: Python Demonstration: Reading and Writing CSV files
- Video: Python Dates and Times
- Video: Advanced Python Objects, map()
- Video: Advanced Python Lambda and List Comprehensions
- Video: Advanced Python Demonstration: The Numerical Python Library (NumPy)
- Video: Introduction
- Notebook: Week 2 Lectures Jupyter Notebook
- Video: The Series Data Structure
- Video: Querying a Series
- Video: The DataFrame Data Structure
- Video: DataFrame Indexing and Loading
- Video: Querying a DataFrame
- Video: Indexing Dataframes
- Video: Missing Values
- Discussion Prompt: The Ethics of Using Hacked Data
- Notebook: Assignment 2
- Notebook: Week 3 Lectures Jupyter Notebook
- Video: Merging Dataframes
- Video: Pandas Idioms
- Video: Group by
- Video: Scales
- Video: Pivot Tables
- Video: Date Functionality
- Discussion Prompt: Goodhart's Law
- Notebook: Assignment 3
- Notebook: Week 4 Lectures Jupyter Notebook
- Video: Introduction
- Video: Distributions
- Video: More Distributions
- Video: Hypothesis Testing in Python
- Discussion Prompt: The End of Theory
- Discussion Prompt: Science Isn't Broken: p-hacking activity
- Notebook: Assignment 4 - Project
- Reading: Post-course Survey
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Very good course for learning python for data science.
Highly recommended for enthusiastic learners
Assignments need to provide better feedback when there is an error
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PK
The lectures were good and the the programming assignments were very challenging.
Thank you very much for a well organised course. :)